Hands-on Introduction to Deep Learning

OHBM 2020 educational course

Deep Learning for Human Brain Mapping

Ariel Rokem, University of Washington

Follow along at: http://arokem.github.io/conv-nets-slides/

Machine learning

Learning from data

Features of the data are extracted/engineered

Training data is used to infer model parameters

Test data is used to evaluate accuracy

Artificial neural networks

A family of machine learning algorithms

Biologically inspired

Minsky and Papert (1969)

A cascade of linear/non-linear operations

Learn by back-propagating the errors

Learn by back-propagating the errors

For the top layer:

How would the error change if we changed the activity in each unit just a little?

For layer L-1

How much would change in each weight affect the activity in layer L?

Depends on:

Stochastic Gradient Descent

Choose a random batch from your training data

Calculate the errors from the top of the network back

Adjust the weights by a small amount

Repeat until convergence

Convolutional networks

Inspired by the visual system

Capitalize on spatial correlations in images

Bosking et al. (1997)

Dumolin & Visin (2018)

MRI Quality control with DL

Anisha Keshavan
(UW eScience → Child Mind Institute → Octave Biosciences)

Jason Yeatman
(UW ILABS → Stanford)

Quality of MRI images is a major bottleneck

Let's get started!

https://bit.ly/ohbm-dl-2020-intro